The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture
Abstract
Package-level integration using multi-chip-modules (MCMs) is a promising approach for building large-scale systems. Compared to a large monolithic die, an MCM combines many smaller chiplets into a larger system, substantially reducing fabrication and design costs. Current MCMs typically only contain a handful of coarse-grained large chiplets due to the high area, performance, and energy overheads associated with inter-chiplet communication. This work investigates and quantifies the costs and benefits of using MCMs with fine-grained chiplets for deep learning inference, an application area with large compute and on-chip storage requirements. To evaluate the approach, we architected, implemented, fabricated, and tested Simba, a 36-chiplet prototype MCM system for deep-learning inference. Each chiplet achieves 4 TOPS peak performance, and the 36-chiplet MCM package achieves up to 128 TOPS and up to 6.1 TOPS/W. The MCM is configurable to support a flexible mapping of DNN layers to the distributed compute and storage units. To mitigate inter-chiplet communication overheads, we introduce three tiling optimizations that improve data locality. These optimizations achieve up to 16% speedup compared to the baseline layer mapping. Our evaluation shows that Simba can process 1988 images/s running ResNet-50 with batch size of one, delivering inference latency of 0.50 ms.
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Self-citationHow to Evaluate Deep Neural Network Processors: TOPS/W (Alone) Considered Harmful
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2020) · IEEE Solid-State Circuits Magazine · doi:10.1109/mssc.2020.3002140 · Cited by 80
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Metadata
- Authors
- Shao, Y. S., Clemons, J., Venkatesan, R., Zimmer, B., Fojtik, M., Jiang, N., Keller, B., Klinefelter, A., Pinckney, N., Raina, P., Tell, S. G., Zhang, Y., Dally, W. J., Emer, J., Gray, C. T., Khailany, B., & Keckler, S. W.
- Venue
- Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture
- Publisher
- ACM
- Published
- 2019-10-12
- Pages
- 14-27
- ISSN
- 1072-4451 (ISSN-L)
- OpenAlex
- W2980104813
- Cited by
- 406
- References
- 58
- FWCI
- 53.97
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Shao, Y. S., Clemons, J., Venkatesan, R., Zimmer, B., Fojtik, M., Jiang, N., Keller, B., Klinefelter, A., Pinckney, N., Raina, P., Tell, S. G., Zhang, Y., Dally, W. J., Emer, J., Gray, C. T., Khailany, B., & Keckler, S. W. (2019). Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture. Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture. https://doi.org/10.1145/3352460.3358302
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